Pandas Data Analysis
Overview
Master data analysis with Pandas, the powerful Python library for data manipulation and analysis. Learn to clean, transform, analyze, and visualize data effectively.
Learning Objectives
- Load and manipulate data from various sources (CSV, Excel, SQL, APIs)
- Clean and transform messy datasets
- Perform exploratory data analysis (EDA)
- Aggregate and group data for insights
- Create compelling visualizations
- Optimize performance for large datasets
Core Topics
1. Pandas DataFrames & Series
- Creating DataFrames from various sources
- Indexing and selecting data (loc, iloc, at, iat)
- Filtering and boolean indexing
- Adding/removing columns and rows
- Data types and conversions
Code Example:
import pandas as pd
import numpy as np
# Create DataFrame
data = {
'name': ['Alice', 'Bob', 'Charlie', 'David'],
'age': [25, 30, 35, 28],
'salary': [50000, 60000, 75000, 55000],
'department': ['IT', 'HR', 'IT', 'Sales']
}
df = pd.DataFrame(data)
# Indexing and filtering
it_employees = df[df['department'] == 'IT']
high_earners = df.loc[df['salary'] > 55000, ['name', 'salary']]
# Adding calculated columns
df['annual_bonus'] = df['salary'] * 0.10
df['age_group'] = pd.cut(df['age'], bins=[0, 30, 40, 100], labels=['Young', 'Mid', 'Senior'])
print(df)2. Data Cleaning & Transformation
- Handling missing data (dropna, fillna, interpolate)
- Removing duplicates
- String operations and text cleaning
- Date/time parsing and manipulation
- Type conversions and casting
- Applying custom functions (apply, map, applymap)
Code Example:
import pandas as pd
# Load data with missing values
df = pd.read_csv('sales_data.csv')
# Handle missing values
df['price'].fillna(df['price'].median(), inplace=True)
df['category'].fillna('Unknown', inplace=True)
df.dropna(subset=['customer_id'], inplace=True)
# Clean text data
df['product_name'] = df['product_name'].str.strip().str.lower()
df['product_name'] = df['product_name'].str.replace('[^a-zA-Z0-9 ]', '', regex=True)
# Convert dates
df['order_date'] = pd.to_datetime(df['order_date'])
df['year'] = df['order_date'].dt.year
df['month'] = df['order_date'].dt.month
# Remove duplicates
df.drop_duplicates(subset=['order_id'], keep='first', inplace=True)
# Apply custom function
def categorize_price(price):
if price < 50:
return 'Low'
elif price < 100:
return 'Medium'
else:
return 'High'
df['price_category'] = df['price'].apply(categorize_price)3. Aggregation & Grouping
- GroupBy operations
- Aggregation functions (sum, mean, count, etc.)
- Pivot tables and cross-tabulation
- Multi-level indexing
- Window functions (rolling, expanding)
Code Example:
import pandas as pd
# Sample sales data
df = pd.read_csv('sales.csv')
# GroupBy aggregation
dept_stats = df.groupby('department').agg({
'salary': ['mean', 'min', 'max'],
'employee_id': 'count'
})
# Multiple groupby
sales_by_region_product = df.groupby(['region', 'product_category'])['sales'].sum()
# Pivot table
pivot = df.pivot_table(
values='sales',
index='product_category',
columns='quarter',
aggfunc='sum',
fill_value=0
)
# Rolling window (moving average)
df['sales_ma_7d'] = df.groupby('product_id')['sales'].transform(
lambda x: x.rolling(window=7, min_periods=1).mean()
)
# Cumulative sum
df['cumulative_sales'] = df.groupby('product_id')['sales'].cumsum()4. Data Visualization
- Matplotlib basics
- Seaborn for statistical plots
- Pandas built-in plotting
- Customizing plots
- Creating dashboards
Code Example:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set style
sns.set_style('whitegrid')
# Load data
df = pd.read_csv('sales_data.csv')
# 1. Line plot - Sales trend over time
df.groupby('month')['sales'].sum().plot(kind='line', figsize=(10, 6))
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales ($)')
plt.show()
# 2. Bar plot - Sales by category
category_sales = df.groupby('category')['sales'].sum().sort_values(ascending=False)
category_sales.plot(kind='bar', figsize=(10, 6))
plt.title('Sales by Category')
plt.xlabel('Category')
plt.ylabel('Total Sales ($)')
plt.xticks(rotation=45)
plt.show()
# 3. Histogram - Price distribution
df['price'].hist(bins=30, figsize=(10, 6))
plt.title('Price Distribution')
plt.xlabel('Price ($)')
plt.ylabel('Frequency')
plt.show()
# 4. Box plot - Salary by department
df.boxplot(column='salary', by='department', figsize=(10, 6))
plt.title('Salary Distribution by Department')
plt.suptitle('')
plt.show()
# 5. Heatmap - Correlation matrix
corr = df[['age', 'salary', 'years_experience']].corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.show()Hands-On Practice
Project 1: Customer Analytics
Analyze customer purchase behavior and segmentation.
Requirements:
- Load customer transaction data
- Clean and prepare dataset
- Calculate RFM (Recency, Frequency, Monetary) metrics
- Customer segmentation
- Visualize insights
- Generate executive summary
Key Skills: Data cleaning, aggregation, visualization
Project 2: Time Series Analysis
Analyze sales trends and forecast future performance.
Requirements:
- Load time series data
- Handle missing dates
- Calculate moving averages
- Identify trends and seasonality
- Detect anomalies
- Create interactive visualizations
Key Skills: Time series operations, rolling windows, plotting
Project 3: Data Quality Report
Build automated data quality assessment tool.
Requirements:
- Check for missing values
- Identify duplicates
- Detect outliers
- Validate data types
- Generate quality metrics
- Export HTML report
Key Skills: Data validation, statistical analysis, reporting
Assessment Criteria
- Load and clean real-world datasets efficiently
- Perform complex data transformations
- Use GroupBy for aggregations
- Create insightful visualizations
- Handle missing and inconsistent data
- Optimize performance for large datasets
- Document analysis with clear explanations
Resources
Official Documentation
- Pandas Docs - Official documentation
- NumPy Docs - NumPy documentation
- Matplotlib Docs - Plotting library
Learning Platforms
- Kaggle - Free Pandas course
- DataCamp - Interactive courses
- Python for Data Analysis - Wes McKinney's book
Tools
- Jupyter Notebook - Interactive development
- Google Colab - Cloud notebooks
- Anaconda - Data science distribution
Next Steps
After mastering Pandas, explore:
- Scikit-learn - Machine learning
- SQL - Database querying
- Apache Spark - Big data processing
- Tableau/Power BI - Business intelligence tools